code: def test_ablation_flip_yields_multi_field_ablations(self):
      ex = {
          "sentence1": "this short movie is awesome",
          "sentence2": "this short movie is great",
      }
      ex_tokens1 = self.ablation_flip.tokenize(ex["sentence1"])
      ex_tokens2 = self.ablation_flip.tokenize(ex["sentence2"])
  
      self.regression_config[ablation_flip.NUM_EXAMPLES_KEY] = 20
      self.regression_config[ablation_flip.REGRESSION_THRESH_KEY] = 2
      self.regression_config[ablation_flip.MAX_ABLATIONS_KEY] = 5
      self.regression_config[ablation_flip.FIELDS_TO_ABLATE_KEY] = [
          "sentence1",
          "sentence2",
      ]
      cfs = self.ablation_flip.generate(
          ex, self.regression_model, None, self.regression_config
      )
  
      # Verify that at least one counterfactual involves ablations across
      # multiple fields.
      multi_field_ablation_found = False
      for cf in cfs:
          cf_tokens1 = self.ablation_flip.tokenize(cf["sentence1"])
          cf_tokens2 = self.ablation_flip.tokenize(cf["sentence2"])
          if (len(cf_tokens1) < len(ex_tokens1)) and (
              len(cf_tokens2) < len(ex_tokens2)
          ):
              multi_field_ablation_found = True
              break
      self.assertTrue(multi_field_ablation_found)
  
imports: from collections.abc import Iterable, Iterator
  from absl.testing import absltest
  from lit_nlp.api import types
  from lit_nlp.components import ablation_flip
  from lit_nlp.examples.models import glue_models
  import numpy
  from lit_nlp.lib import file_cache
label_desc: Use image node_id 9 for calling the ModelBasedAblationFlipTest obj's underlying member method code with example usage: obj.test_ablation_flip_yields_multi_field_ablations() without return types